| from transformers import AutoFeatureExtractor | |
| from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker | |
| from PIL import Image | |
| import numpy as np | |
| def check_safety(x_image): | |
| safety_model_id = "CompVis/stable-diffusion-safety-checker" | |
| safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id) | |
| safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id) | |
| safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt") | |
| x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values) | |
| assert x_checked_image.shape[0] == len(has_nsfw_concept) | |
| for i in range(len(has_nsfw_concept)): | |
| if has_nsfw_concept[i]: | |
| x_checked_image[i] = load_replacement(x_checked_image[i]) | |
| return x_checked_image, has_nsfw_concept | |
| def numpy_to_pil(images): | |
| """ | |
| Convert a numpy image or a batch of images to a PIL image. | |
| """ | |
| if images.ndim == 3: | |
| images = images[None, ...] | |
| images = (images * 255).round().astype("uint8") | |
| pil_images = [Image.fromarray(image) for image in images] | |
| return pil_images | |
| def load_replacement(x): | |
| try: | |
| hwc = x.shape | |
| y = Image.open("html/NSFW_replace.jpg").convert("RGB").resize((hwc[1], hwc[0])) | |
| y = (np.array(y)/255.0).astype(x.dtype) | |
| assert y.shape == x.shape | |
| return y | |
| except Exception as e: | |
| return x |